Reprogramming electronic noses to get a whiff of new smells

To get better performance out of an electronic odorant sensor, researchers …

It's easy for our noses to distinguish between something that smells so delicious it brings us back to the happy days of childhood in Grandma's warm kitchen and something that smells so nasty that we need to leave the room. But the biology that produces those responses is very complex. Each olfactory receptor expressed in a neuron in a human’s nose can recognize multiple odorants, and many odorants are recognized by more than one receptor, so matching which receptor will respond to which odorant has been daunting. Now, researchers have made progress in teaching electronic noses to make the same sorts of distinctions.

E-noses are analytic devices containing an array of chemical sensors. When these sensors are stimulated by an odorant molecule, they create a response pattern. E-noses have been used in medical, environmental, and industrial applications. Like a dog sniffing out cocaine, they must first be trained with odor samples to generate a reference database of odors to compare new smells against. However, they have proven incapable of dealing with a new odorant that was not present in the initial dataset.

Recently, computer scientists made an e-nose that can predict and mimic how an olfactory receptor will respond to a particular scent. Instead of training the e-nose to recognize a set of scents, they tuned it to the receptive range of an actual olfactory receptor. The e-nose predicted whether twelve odorant receptors from Drosophila would respond to 21 new odorants with roughly 75 percent accuracy.

Although this e-nose can predict which odorant will stimulate a given receptor, it cannot say why—for instance, what molecular features, like size, shape, or charge, it may possess that enable it to interact with its receptor. The authors claim this is an advantage: first, because this method can be used to analyze mixtures of odorants, whereas methods that assess response based on molecular features cannot. Second, looking for molecular features common to odorants that are grouped by an eNose can facilitate the identification of those that are important and help identify new ligands. This method can be expanded to study all ligand-receptor interactions, and isn’t limited to those in olfaction.